| Challenge: | Opusparcus is a new corpus of paraphrases for six European languages . it is based on movie and TV subtitles, which are colloquial and informal . |
| Approach: | They propose to use opensubtitles2016 paraphrase corpus for six European languages . they extract paraphrases from movie and TV subtitles from the corpus . |
| Outcome: | The new corpus is available in German, English, Finnish, French, Russian, and Swedish . it is extracted from the OpenSubtitles2016 corpus, which contains subtitles from movies and TV shows . |
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| Challenge: | a new study investigates the quality and novelty of generated paraphrases . paraphrase models can be used for information retrieval and data mining . |
| Approach: | They use state-of-the-art neural machine translation models trained on the Opusparcus corpus to generate paraphrases in six languages. |
| Outcome: | The proposed model outperforms the existing model on human evaluation in five of the six languages. |
TaPaCo: A Corpus of Sentential Paraphrases for 73 Languages (2020.lrec-1)
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| Challenge: | a crowdsourcing project aimed at language learners has created a paraphrase corpus for 73 languages . the corpus contains 1.9 million sentences, with 200 - 250 000 sentences per language . |
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Paraphrastic Representations at Scale (2022.emnlp-demos)
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| Challenge: | a new system allows users to train their own state-of-the-art paraphrastic sentence representations in a variety of languages. |
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Paraphrases as Foreign Languages in Multilingual Neural Machine Translation (P19-2)
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OpenSubtitles2018: Statistical Rescoring of Sentence Alignments in Large, Noisy Parallel Corpora (L18-1)
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| Challenge: | Movie and TV subtitles are a valuable resource for the compilation of parallel corpora . however, the quality of the resulting sentence alignments is often lower than for other parallel corpoora. |
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ParaTag: A Dataset of Paraphrase Tagging for Fine-Grained Labels, NLG Evaluation, and Data Augmentation (2022.emnlp-main)
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| Challenge: | Existing datasets only annotate a binary label for each sentence pair. Existing models only annnotate binary labels for each phrase pair. |
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ParaSCI: A Large Scientific Paraphrase Dataset for Longer Paraphrase Generation (2021.eacl-main)
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| Challenge: | Existing paraphrase datasets are mainly from news, novels, or social media platforms. |
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Multilingual Whispers: Generating Paraphrases with Translation (D19-55)
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| Challenge: | Humans naturally paraphrase, but they can generate approximately the same meaning with a different surface realization. |
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A Large Resource of Patterns for Verbal Paraphrases (L18-1)
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| Challenge: | Xu et al., 2015: paraphrases play an important role in natural language understanding . he says it is difficult to propose a paraphrasing relation for natural language processing systems . |
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ParaAMR: A Large-Scale Syntactically Diverse Paraphrase Dataset by AMR Back-Translation (2023.acl-long)
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| Challenge: | Paraphrase generation is a long-standing task in natural language processing (NLP). |
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